// Copyright (c) 2021 by Rockchip Electronics Co., Ltd. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

/*-------------------------------------------
                Includes
-------------------------------------------*/
#include "rknn_api.h"

#include <float.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <sys/time.h>
#include <vector>

#define STB_IMAGE_IMPLEMENTATION // 添加这个宏定义，让其只包含相关函数定义的源码
#include "stb/stb_image.h"       //用于图像加载
#define STB_IMAGE_RESIZE_IMPLEMENTATION
#include <stb/stb_image_resize.h> //用于图像将采样

// https://www.jianshu.com/p/20d1160b4e79

#include "postprocess.h"

#define PERF_WITH_POST 1

/*-------------------------------------------
                  Functions
-------------------------------------------*/
static inline int64_t getCurrentTimeUs()
{
    struct timeval tv;
    gettimeofday(&tv, NULL);
    return tv.tv_sec * 1000000 + tv.tv_usec;
}

static void dump_tensor_attr(rknn_tensor_attr *attr)
{
    char dims[128] = {0};
    for (int i = 0; i < attr->n_dims; ++i)
    {
        int idx = strlen(dims);
        sprintf(&dims[idx], "%d%s", attr->dims[i], (i == attr->n_dims - 1) ? "" : ", ");
    }
    printf("  index=%d, name=%s, n_dims=%d, dims=[%s], n_elems=%d, size=%d, fmt=%s, type=%s, qnt_type=%s, "
           "zp=%d, scale=%f\n",
           attr->index, attr->name, attr->n_dims, dims, attr->n_elems, attr->size, get_format_string(attr->fmt),
           get_type_string(attr->type), get_qnt_type_string(attr->qnt_type), attr->zp, attr->scale);
}

static void *load_file(const char *file_path, size_t *file_size)
{
    FILE *fp = fopen(file_path, "r");
    if (fp == NULL)
    {
        printf("failed to open file: %s\n", file_path);
        return NULL;
    }

    fseek(fp, 0, SEEK_END);
    size_t size = (size_t)ftell(fp);
    fseek(fp, 0, SEEK_SET);

    void *file_data = malloc(size);
    if (file_data == NULL)
    {
        fclose(fp);
        printf("failed allocate file size: %zu\n", size);
        return NULL;
    }

    if (fread(file_data, 1, size, fp) != size)
    {
        fclose(fp);
        free(file_data);
        printf("failed to read file data!\n");
        return NULL;
    }

    fclose(fp);

    *file_size = size;

    return file_data;
}

static unsigned char *load_image(const char *image_path, rknn_tensor_attr *input_attr, int *img_height, int *img_width)
{
    int req_height = 0;
    int req_width = 0;
    int req_channel = 0;

    switch (input_attr->fmt)
    {
    case RKNN_TENSOR_NHWC:
        req_height = input_attr->dims[1];
        req_width = input_attr->dims[2];
        req_channel = input_attr->dims[3];
        break;
    case RKNN_TENSOR_NCHW:
        req_height = input_attr->dims[2];
        req_width = input_attr->dims[3];
        req_channel = input_attr->dims[1];
        break;
    default:
        printf("meet unsupported layout\n");
        return NULL;
    }

    int channel = 0;
    // 图片路径、图片宽、图片高、通道（rgb=3）
    unsigned char *image_data = stbi_load(image_path, img_width, img_height, &channel, req_channel);
    if (image_data == NULL)
    {
        printf("load image failed!\n");
        return NULL;
    }

    if (*img_width != req_width || *img_height != req_height)
    {
        unsigned char *image_resized = (unsigned char *)STBI_MALLOC(req_width * req_height * req_channel);
        if (!image_resized)
        {
            printf("malloc image failed!\n");
            STBI_FREE(image_data);
            return NULL;
        }
        // 图像缩放 输入图像数据指针、输入图像宽、输入图像宽、输入图像步长、输出图像数据指针、输出图像宽、输出图像高、输出图像步长
        if (stbir_resize_uint8(image_data, *img_width, *img_height, 0, image_resized, req_width, req_height, 0, channel) != 1)
        {
            printf("resize image failed!\n");
            STBI_FREE(image_data);
            return NULL;
        }
        STBI_FREE(image_data);
        image_data = image_resized;
    }

    return image_data;
}

// 量化模型的npu输出结果为int8数据类型，后处理要按照int8数据类型处理
// 如下提供了int8排布的NC1HWC2转换成int8的nchw转换代码
int NC1HWC2_int8_to_NCHW_int8(const int8_t *src, int8_t *dst, int *dims, int channel, int h, int w)
{
    int batch = dims[0];
    int C1 = dims[1];
    int C2 = dims[4];
    int hw_src = dims[2] * dims[3];
    int hw_dst = h * w;
    for (int i = 0; i < batch; i++)
    {
        src = src + i * C1 * hw_src * C2;
        dst = dst + i * channel * hw_dst;
        for (int c = 0; c < channel; ++c)
        {
            int plane = c / C2;
            const int8_t *src_c = plane * hw_src * C2 + src;
            int offset = c % C2;
            for (int cur_h = 0; cur_h < h; ++cur_h)
                for (int cur_w = 0; cur_w < w; ++cur_w)
                {
                    int cur_hw = cur_h * w + cur_w;
                    dst[c * hw_dst + cur_h * w + cur_w] = src_c[C2 * cur_hw + offset];
                }
        }
    }

    return 0;
}

// 量化模型的npu输出结果为int8数据类型，后处理要按照int8数据类型处理
// 如下提供了int8排布的NC1HWC2转换成float的nchw转换代码
int NC1HWC2_int8_to_NCHW_float(const int8_t *src, float *dst, int *dims, int channel, int h, int w, int zp, float scale)
{
    int batch = dims[0];
    int C1 = dims[1];
    int C2 = dims[4];
    int hw_src = dims[2] * dims[3];
    int hw_dst = h * w;
    for (int i = 0; i < batch; i++)
    {
        src = src + i * C1 * hw_src * C2;
        dst = dst + i * channel * hw_dst;
        for (int c = 0; c < channel; ++c)
        {
            int plane = c / C2;
            const int8_t *src_c = plane * hw_src * C2 + src;
            int offset = c % C2;
            for (int cur_h = 0; cur_h < h; ++cur_h)
                for (int cur_w = 0; cur_w < w; ++cur_w)
                {
                    int cur_hw = cur_h * w + cur_w;
                    dst[c * hw_dst + cur_h * w + cur_w] = (src_c[C2 * cur_hw + offset] - zp) * scale; // int8-->float
                }
        }
    }

    return 0;
}

/*-------------------------------------------
                  Main Functions
-------------------------------------------*/
int main(int argc, char *argv[])
{
    if (argc < 3)
    {
        printf("Usage:%s model_path input_path [loop_count]\n", argv[0]);
        return -1;
    }

    char *model_path = argv[1];
    char *input_path = argv[2];

    int loop_count = 1;
    if (argc > 3)
    {
        loop_count = atoi(argv[3]);
    }

    const float nms_threshold = 0.45;
    const float box_conf_threshold = 0.25;

    int img_width = 320;
    int img_height = 320;

    rknn_context ctx = 0;

    // Load RKNN Model
#if 1
    // Init rknn from model path
    int ret = rknn_init(&ctx, model_path, 0, 0, NULL); // 初始化上下文
    printf("===init model===\n");
#else
    // Init rknn from model data
    size_t model_size;
    void *model_data = load_file(model_path, &model_size);
    if (model_data == NULL)
    {
        return -1;
    }
    int ret = rknn_init(&ctx, model_data, model_size, 0, NULL);
    free(model_data);
#endif
    if (ret < 0)
    {
        printf("rknn_init fail! ret=%d\n", ret);
        return -1;
    }

    // Get sdk and driver version
    rknn_sdk_version sdk_ver; // 查询sdk版本号
    ret = rknn_query(ctx, RKNN_QUERY_SDK_VERSION, &sdk_ver, sizeof(sdk_ver));
    if (ret != RKNN_SUCC)
    {
        printf("rknn_query fail! ret=%d\n", ret);
        return -1;
    }
    printf("rknn_api/rknnrt version: %s, driver version: %s\n", sdk_ver.api_version, sdk_ver.drv_version);

    // Get Model Input Output Info
    rknn_input_output_num io_num; // 查询输入输出属性
    ret = rknn_query(ctx, RKNN_QUERY_IN_OUT_NUM, &io_num, sizeof(io_num));
    if (ret != RKNN_SUCC)
    {
        printf("rknn_query fail! ret=%d\n", ret);
        return -1;
    }
    printf("model input num: %d, output num: %d\n", io_num.n_input, io_num.n_output);

    printf("input tensors:\n");
    // 输入tensor的属性
    rknn_tensor_attr input_attrs[io_num.n_input];
    memset(input_attrs, 0, io_num.n_input * sizeof(rknn_tensor_attr));
    for (uint32_t i = 0; i < io_num.n_input; i++)
    {
        input_attrs[i].index = i;
        // query info
        ret = rknn_query(ctx, RKNN_QUERY_INPUT_ATTR, &(input_attrs[i]), sizeof(rknn_tensor_attr));
        if (ret < 0)
        {
            printf("rknn_init error! ret=%d\n", ret);
            return -1;
        }
        dump_tensor_attr(&input_attrs[i]);
    }

    printf("output tensors:\n");
    // 输出tensor的属性
    rknn_tensor_attr output_attrs[io_num.n_output];
    memset(output_attrs, 0, io_num.n_output * sizeof(rknn_tensor_attr));
    for (uint32_t i = 0; i < io_num.n_output; i++)
    {
        output_attrs[i].index = i;
        // query info
        ret = rknn_query(ctx, RKNN_QUERY_NATIVE_OUTPUT_ATTR, &(output_attrs[i]), sizeof(rknn_tensor_attr));
        if (ret != RKNN_SUCC)
        {
            printf("rknn_query fail! ret=%d\n", ret);
            return -1;
        }
        dump_tensor_attr(&output_attrs[i]);
    }

    // Get custom string
    // rknn_custom_string custom_string;
    // ret = rknn_query(ctx, RKNN_QUERY_CUSTOM_STRING, &custom_string, sizeof(custom_string));
    // if (ret != RKNN_SUCC)
    // {
    //   printf("rknn_query fail! ret=%d\n", ret);
    //   return -1;
    // }
    // printf("custom string: %s\n", custom_string.string);

    unsigned char *input_data = NULL;
    rknn_tensor_type input_type = RKNN_TENSOR_UINT8;
    rknn_tensor_format input_layout = RKNN_TENSOR_NHWC;

    // Load image

    // input_data = load_image(input_path, &input_attrs[0], &img_height, &img_width);
    // printf("==load image height is %d, width is %d===\n", img_height, img_width);

    // load test file
    input_data = new unsigned char[input_attrs[0].size];

    FILE *fp = fopen(input_path, "rb");
    printf("==load fiel is %s==\n", input_path);
    if (fp == NULL)
    {
        perror("open failed!");
        return -1;
    }
    fread(input_data, input_attrs[0].size, 1, fp); // 读取输入数据
    fclose(fp);

    if (!input_data)
    {
        return -1;
    }

    // Create input tensor memory
    rknn_tensor_mem *input_mems[1];
    // default input type is int8 (normalize and quantize need compute in outside)
    // if set uint8, will fuse normalize and quantize to npu
    input_attrs[0].type = input_type;
    // default fmt is NHWC, npu only support NHWC in zero copy mode
    input_attrs[0].fmt = input_layout;

    input_mems[0] = rknn_create_mem(ctx, input_attrs[0].size_with_stride);

    // Copy input data to input tensor memory
    int width = input_attrs[0].dims[2];
    int stride = input_attrs[0].w_stride;

    printf("===width is %d, stride is %d====\n", width, stride);
    if (width == stride)
    {
        memcpy(input_mems[0]->virt_addr, input_data, width * input_attrs[0].dims[1] * input_attrs[0].dims[3]);
        printf("===input data len is %d===\n");
    }
    else
    {
        int height = input_attrs[0].dims[1];
        int channel = input_attrs[0].dims[3];
        // copy from src to dst with stride
        uint8_t *src_ptr = input_data;
        uint8_t *dst_ptr = (uint8_t *)input_mems[0]->virt_addr;
        // width-channel elements
        int src_wc_elems = width * channel;
        int dst_wc_elems = stride * channel;
        for (int h = 0; h < height; ++h)
        {
            memcpy(dst_ptr, src_ptr, src_wc_elems);
            src_ptr += src_wc_elems;
            dst_ptr += dst_wc_elems;
        }
    }

    // Create output tensor memory
    rknn_tensor_mem *output_mems[io_num.n_output];
    for (uint32_t i = 0; i < io_num.n_output; ++i)
    {
        output_mems[i] = rknn_create_mem(ctx, output_attrs[i].size_with_stride);
    }

    // Set input tensor memory
    ret = rknn_set_io_mem(ctx, input_mems[0], &input_attrs[0]);
    if (ret < 0)
    {
        printf("rknn_set_io_mem fail! ret=%d\n", ret);
        return -1;
    }

    // Set output tensor memory
    for (uint32_t i = 0; i < io_num.n_output; ++i)
    {
        // set output memory and attribute
        ret = rknn_set_io_mem(ctx, output_mems[i], &output_attrs[i]);
        if (ret < 0)
        {
            printf("rknn_set_io_mem fail! ret=%d\n", ret);
            return -1;
        }
    }

    // Run
    printf("Begin perf ...\n");
    for (int i = 0; i < loop_count; ++i)
    {
        int64_t start_us = getCurrentTimeUs();
        printf("===start run===\n");
        ret = rknn_run(ctx, NULL);

        printf("====end run===\n");
        int64_t elapse_us = getCurrentTimeUs() - start_us;
        if (ret < 0)
        {
            printf("rknn run error %d\n", ret);
            return -1;
        }
        printf("%4d: Elapse Time = %.2fms, FPS = %.2f\n", i, elapse_us / 1000.f, 1000.f * 1000.f / elapse_us);
    }

    printf("output origin tensors:\n");
    rknn_tensor_attr orig_output_attrs[io_num.n_output];
    memset(orig_output_attrs, 0, io_num.n_output * sizeof(rknn_tensor_attr));

    for (uint32_t i = 0; i < io_num.n_output; i++)
    {
        orig_output_attrs[i].index = i;
        // query info
        ret = rknn_query(ctx, RKNN_QUERY_OUTPUT_ATTR, &(orig_output_attrs[i]), sizeof(rknn_tensor_attr));
        if (ret != RKNN_SUCC)
        {
            printf("rknn_query fail! ret=%d\n", ret);
            return -1;
        }
        dump_tensor_attr(&orig_output_attrs[i]);
    }

    float *output_mems_nchw[io_num.n_output]; // 原始输出
    int size = output_attrs[0].size_with_stride * sizeof(float);
    output_mems_nchw[0] = (float *)malloc(size); // 赋予内存大小
    

    int8_t *src = (int8_t *)output_mems[0]->virt_addr;
    for (int index = 0; index < output_attrs[0].n_elems; index++)
    {
        float tmp =  (float)(src[index] - output_attrs[0].zp) * output_attrs[0].scale;
        output_mems_nchw[0][index] = (tmp + 0.5)*320 - 0.5;
        printf("===float num is %d, data is %d, float data is %f==\n", index, src[index], output_mems_nchw[0][index]);
    }


    // Destroy rknn memory
    rknn_destroy_mem(ctx, input_mems[0]);
    for (uint32_t i = 0; i < io_num.n_output; ++i)
    {
        rknn_destroy_mem(ctx, output_mems[i]);
        // free(output_mems_nchw[i]);
    }

    // destroy
    rknn_destroy(ctx);

    free(input_data);

    return 0;
}
